Image quality compliance tool

ABSTRACT

The technology relates to a methods and systems for improving medical imaging procedures. An example method includes receiving a first set of quality metrics for a plurality of medical images acquired at a first imaging facility; receiving a second set of quality metrics for a second plurality of medical images acquired at a second imaging facility; comparing the first set of quality metrics to the second set of quality metrics; based on the comparison of the first set of quality metrics to the second set of quality metrics, generating a benchmark for at least one metric in the first set of quality metrics and the second set of quality metrics; generating facility data based on the generated benchmark and the first set of quality metrics; and sending the facility data to the first imaging facility.

RELATED APPLICATIONS

This application is a continuation of application Ser. No. 17/673,936,filed Feb. 17, 2022, which is a continuation of application Ser. No.16/779,153, filed Jan. 31, 2020, now U.S. Pat. No. 11,419,569, whichapplication is a continuation-in-part of International ApplicationPCT/IB2018/056208, with an international filing date of Aug. 16, 2018,which claims priority to U.S. Provisional Patent Application Ser. No.62/546,167, titled “Techniques for Breast Imaging Patient MotionArtifact Compensation” and filed on Aug. 16, 2017. The contents of theaforementioned applications are incorporated herein by reference intheir entireties and, to the extent appropriate, priority is claimed tothe aforementioned applications.

FIELD OF THE DISCLOSURE

The disclosure generally relates to quality assurance of patientimaging, and more particularly to improving detection of movement andcorrection of motion artifacts, such as it relates to mammography ortomosynthesis image acquisition.

BACKGROUND

Preventing movement of subject tissue, and in particular breast tissue,is important when performing radiation-based imaging of a patient for avariety of reasons. First, some imaging procedures last for anon-trivial period of time, and movement during a portion of theprocedure may negatively impact image quality. Specifically, patientmotion may cause anatomical distortions or artifacts, which can beexaggerated during longer exposure times. Second, it is desirable tominimize a patient's total exposure to radiation during a procedure and,thus, subsequent imaging to obtain proper image quality is not ideal.Third, due to regulations in many jurisdictions, subsequent imaging usedsolely to correct image quality may be counted against a practitioner ororganization, and frequent re-imaging may result in revocation of alicense and/or accreditation. Fourth, poor quality images due to excessmovement may require a patient to make subsequent visits to an imagingcenter, placing additional burden on the patient and the healthcaresystem itself, including the imaging center and payer.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some novel embodiments described herein. This summaryis not an extensive overview, and it is not intended to identifykey/critical elements or to delineate the scope thereof. Its solepurpose is to present some concepts in a simplified form as a prelude tothe more detailed description that is presented later.

Techniques for detecting and/or otherwise notifying a patient ofdetected motion and modifying the imaging protocol during breast imagingare described. As described above, preventing movement breast tissue, isimportant when performing radiation-based imaging of a patient for avariety of reasons including improving image quality, improving patientexperience, reducing exposure and avoiding repeat imaging. For at leastthese reasons, there is a need for improved techniques, which may beautomated or semi-automated, for detection of movement during an imagingprocedure, for corrective actions during and after the procedure whenmovement has been detected, and for minimizing the amount of radiationexposure to patients in a workflow efficient manner.

An imaging system as described herein may include an imaging detector tocapture an image of human tissue, such as breast tissue, and acompression paddle situated apart from the imaging detector to compressthe human tissue between the compression paddle and the imagingdetector. One or more sensors may be included, in one embodiment a forcesensor may generate a force signal indicating a measure of force appliedto the human tissue. A movement detection circuit may filter a movementsignal from the force signal indicating a measure of movement of thecompressed human tissue. A movement analysis module may determine thatthe movement signal is beyond a movement threshold. An image correctionmodule may perform a corrective action based upon the determination thatthe movement signal is beyond a movement threshold. Other embodimentsare described and claimed.

The force sensor described herein is typical to most modern mammographysystems where breast compression force is incorporated. The force sensorhelps to prevent excessive compression of the patient's breast which cancause pain and other undesirable effects. The embodiments as describedand claimed relate to the output of the force sensor, representative ofa force level, which may be filtered or converted by one or morecircuits or modules described herein into a value that indicatesmovement. This movement signal, when compared to other measurements overtime, may indicate movement of the patient undergoing an imagingprocedure.

In addition or in the alternative, other sensors may be used. Forexample, one or more ultrasound sensors, optical and/or infrared sensorsmay be used. In some examples, the sensors may be located either in agrid on the compression paddle. In other examples, the sensors may belocated on the periphery of the paddle. The sensors may capture spatialdata information from the compression of the breast. The specialinformation may be used to create motion maps and/or contact maps. Themotion map information can be used to create a correction map. Thecorrection map information may be used as input to the image correctionalgorithm which corrects the tomosynthesis images. In the examples wherea contact map is created based on the spatial information, the contactmap can be used to create compression contours, which can be used as aninput to the compression adequacy analysis and recommend a correctiveaction.

Some software based techniques for detecting motion during an imagingprocedure have been previously described. For example, one method ofdetecting patient motion includes detecting from a series of imagesdisplacement of an edge line such as the skin line of the breast, animplant edge, or some other internal edge. This skin line detectionprocess is disclosed in U.S. Pat. No. 9,498,180, titled System andMethod For Detecting Patient Motion During Tomosynthesis Scans, which isincorporated by reference herein (hereafter the '180 Patent).

However, unlike software based and image artifact based motiondetection, detection of motion based on hardware sensors gives anobjective measure of patient motion to add to the assessment of motion.The independent, hardware based, detection using the information fromone or more sensors allows for greater accuracy. In addition, becausethe mammography system already includes the force sensor, this method ofpatient motion is more cost effective than the alternative image baseddetection when force sensor detection is used. In addition, differenttypes of motion may be detected and different compensation actions maybe taken. For example, if motion with regular movement interval isdetected, such as breathing or heartbeat, image capture may besynchronized with the motion. In a different example, if irregularmovement is detected, such as patient adjusting position, the imagecapture may be delayed. Such nuanced and continued detection may not bepossible if the detection is based on image processing alone.

In an aspect, the technology relates to a method for improving medicalimaging procedures. The method includes receiving, by a central computersystem from a first imaging facility, a first set of quality metrics fora plurality of medical images acquired at the first imaging facility;receiving, by the central computer system from a second imagingfacility, a second set of quality metrics for a second plurality ofmedical images acquired at the second imaging facility; comparing, bythe central computer system, the first set of quality metrics to thesecond set of quality metrics; based on the comparison of the first setof quality metrics to the second set of quality metrics, generating, bythe central computer system, a benchmark for at least one metric in thefirst set of quality metrics and the second set of quality metrics;generating, by the central computer system, facility data based on thegenerated benchmark and the first set of quality metrics; and sending,by the central computer system, the facility data to the first imagingfacility.

In an example, the method further includes generating a trainingrecommendation based on the generated benchmark and the first set ofquality metrics; receiving, from the first imaging facility, asubsequent set of quality metrics for a plurality of medical imagesacquired at the first facility after the sending of the generatedtraining recommendation; comparing the subsequent set of quality metricsto the first set of quality metrics; and based on the comparison of thesubsequent set of quality metrics to the first set of quality metrics,generating an effectiveness rating for the generated training. Inanother example, the method further includes receiving, from the firstimaging facility, a subsequent set of quality metrics for a plurality ofmedical images acquired at the first facility after the sending of thegenerated training recommendation; comparing the subsequent set ofquality metrics to the first set of quality metrics to determine a trendfor at least one quality metric; and based on determined trend for theat least one quality metric, generating a trend warning. In yet anotherexample, the quality metrics are based on positioning metrics generatedfrom the plurality of medical images. In still another example, themethod further includes providing the first set of quality metrics andthe second set of quality metrics as inputs to an unsupervised machinelearning algorithm to identify additional patterns within the sets ofquality metrics. In a further example, the first set of quality metricsare received via a web application managed by the central computersystem and the training is sent via the web application. In still yetanother example, the quality metrics are based on patient movement. Inanother example, at least one quality metric is based on a movementsignal that is generated by the following operations: generating, by aforce sensor, a force signal indicating a measure of force appliedsuperior to human tissue being compressed between a compression paddleand an imaging detector to capture an image of the human tissue; andfiltering, by a movement detection circuit, a movement signal from theforce signal indicating a measure of movement of the compressed humantissue.

In another aspect, the technology relates to a central computer systemthat includes at least one processing unit; and memory operatively incommunication with the at least processing unit, the memory storinginstructions that, when executed by the at least one processing unit,are configured to cause the system to perform a set of operations. Theoperations include receiving, from a first imaging facility, a first setof quality metrics for a plurality of medical images acquired at thefirst imaging facility; receiving, from a second imaging facility, asecond set of quality metrics for a second plurality of medical imagesacquired at the second imaging facility; comparing, by the centralcomputer system, the first set of quality metrics to the second set ofquality metrics; based on the comparison of the first set of qualitymetrics to the second set of quality metrics, generating a benchmark forat least one metric in the first set of quality metrics and the secondset of quality metrics; generating a training recommendation based onthe generated benchmark and the first set of quality metrics; andsending the generated training recommendation to the first facility.

In an example, the operations further comprise receiving, from the firstimaging facility, a subsequent set of quality metrics for a plurality ofmedical images acquired at the first facility after the sending of thegenerated training recommendation; comparing the subsequent set ofquality metrics to the first set of quality metrics; and based on thecomparison of the subsequent set of quality metrics to the first set ofquality metrics, generating an effectiveness rating for the generatedtraining. In another example, the operations further comprise receiving,from the first imaging facility, a subsequent set of quality metrics fora plurality of medical images acquired at the first facility after thesending of the generated training recommendation; comparing thesubsequent set of quality metrics to the first set of quality metrics todetermine a trend for at least one quality metric; and based ondetermined trend for the at least one quality metric, generating a trendwarning. In yet another example, the trend warning is based on a rate ofthe determined trend. In still yet another example, the operationsfurther comprise providing the first set of quality metrics and thesecond set of quality metrics as inputs to an unsupervised machinelearning algorithm to identify additional patterns within the sets ofquality metrics. In another example, the first set of quality metricsare received via a web application managed by the central computersystem and the training is sent via the web application.

In a further example, the set of operations further comprise providing adashboard via a web application to the first facility and the secondfacility. In still another example, the dashboard displays qualitymetrics received from the first facility compared to the benchmark. Instill yet another example, receiving the first set of quality metricsfor a plurality of medical images includes receiving identificationinformation for the plurality of medical images.

In another aspect, the technology relates to a computer-implementedmethod comprising for improving medical imaging procedures. The methodincludes receiving patient positioning scores from a technician at afirst facility; receiving patient positioning scores from a technicianat a second facility; comparing a threshold to the received patientpositioning scores for the technician at the first facility and thetechnician at the second facility; based on the comparison, determiningthat the patient positioning scores for the technician at the firstfacility are below the threshold; and based on the patient positioningscores for the technician at the first facility being below thethreshold, generating a recommendation for a corrective action for thetechnician.

In an example, the method further includes based on the comparison,determining that the patient positioning scores for the technician atthe second facility are above the threshold; and based on the patientpositioning scores for the technician at the second facility being abovethe threshold, generating a report of compliance with federalregulations. In another example, the method includes comparing thepatient positioning scores from the technician at the first facility tothe patient positioning scores from the technician at the secondfacility to score the technologists relative to each other.

To the accomplishment of the foregoing and related ends, certainillustrative aspects are described herein in connection with thefollowing description and the annexed drawings. These aspects areindicative of the various ways in which the principles disclosed hereincan be practiced and all aspects and equivalents thereof are intended tobe within the scope of the claimed subject matter. Other advantages andnovel features will become apparent from the following detaileddescription when considered in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an embodiment of an imaging system.

FIG. 2 illustrates an embodiment of an imaging system.

FIG. 3 illustrates an embodiment of an imaging system.

FIG. 4 illustrates a logic flow according to an embodiment.

FIG. 5 illustrates a logic flow according to an embodiment.

FIG. 6 illustrates a logic flow according to an embodiment.

FIG. 7 illustrates a generated image according to an embodiment.

FIG. 8A illustrates a generated image according to an embodiment.

FIG. 8B illustrates a system of facilities according to an embodiment.

FIG. 8C illustrates a logic flow according to an embodiment.

FIG. 8D illustrates a logic flow according to an embodiment.

FIG. 8E illustrates a logic flow according to an embodiment.

FIG. 8F illustrates a logic flow according to an embodiment.

FIG. 9 illustrates an article of manufacture according to an embodiment.

FIG. 10 illustrates an embodiment of a centralized system.

FIG. 11 illustrates an embodiment of a distributed system.

FIG. 12 illustrates an embodiment of a computing architecture.

FIG. 13 illustrates an embodiment of a communications architecture.

FIG. 14A illustrates an embodiment of an imaging system.

FIG. 14B illustrates an example contact map according to an embodiment.

FIGS. 15A-C illustrate a logic flow according to an embodiment.

DETAILED DESCRIPTION

Techniques for breast imaging patient motion compensation, compressionevaluation, and positioning evaluation are described. An imaging systemmay include an imaging detector to capture an image of human tissue,such as breast tissue or other soft tissue, and a compression paddlesituated apart from the imaging detector to compress the human tissuebetween the compression paddle and the imaging detector. In oneembodiment, a force sensor may generate a force signal indicating ameasure of force applied to the human tissue. A movement detectioncircuit may filter a movement signal from the force signal indicating ameasure of movement of the compressed human tissue. A movement analysismodule may determine that the movement signal is beyond a movementthreshold. An image correction module to perform a corrective actionbased upon the determination that the movement signal is beyond amovement threshold. In another embodiment, other types of sensors may beused which may be disposed in a grid or around the periphery of thecompression paddle.

As used herein, corrective actions may include actions to correct animage, generate an image while minimizing motion artifacts, generate anaudio or visual indication that motion has been detected, and/or otheractions described below in response to detection of motion during aprocedure. By way of example and not limitation, corrective actions mayinclude the determination and display of a movement score on a displaydevice, display of an alert on a display device indicating that amovement threshold has been exceeded, triggering a visual indicator ofthe imaging system, terminating or modifying an imaging sequence orimaging protocol or image acquisition, delaying capture of the image ofhuman tissue until the movement threshold is no longer exceeded, and/orsynchronizing an image capture with repetitive movement. A movementscore for all images taken by a particular technologist may be combinedto create a positioning score for the technologist. The movement scoresmay be compared to other technologists in a facility or in otherfacilities. The technologist score may be compared to a threshold todetermine compliance. A facility score may be compared to otherfacilities and compared to a threshold score to determine compliance. Areport may be generated showing positioning scores for the technologist,the facility and compliance over time. A retrospective and prospectiveapproach will allow the facility to identify the root-cause for why thepositioning, noise, artifacts, compression etc. at the physician levelcould occur. A particular technician can be identified with thisapproach to understand his/her behavior to improve their ability to taketheir image. Other embodiments are described and claimed.

With general reference to notations and nomenclature used herein, thedetailed descriptions which follow may be presented in terms of programprocedures executed on a computer or network of computers. Theseprocedural descriptions and representations are used by those skilled inthe art to most effectively convey the substance of their work to othersskilled in the art.

A procedure is here, and generally, conceived to be a self-consistentsequence of operations leading to a desired result. These operations arethose requiring physical manipulations of physical quantities. Usually,though not necessarily, these quantities take the form of electrical,magnetic or optical signals capable of being stored, transferred,combined, compared, and otherwise manipulated. It proves convenient attimes, principally for reasons of common usage, to refer to thesesignals as bits, values, elements, symbols, characters, terms, numbers,or the like. It should be noted, however, that all of these and similarterms are to be associated with the appropriate physical quantities andare merely convenient labels applied to those quantities.

Further, the manipulations performed are often referred to in terms,such as adding or comparing, which are commonly associated with mentaloperations performed by a human operator. No such capability of a humanoperator is necessary, or desirable in most cases, in any of theoperations described herein which form part of one or more embodiments.Rather, the operations are machine operations. Useful machines forperforming operations of various embodiments include general purposedigital computers or similar devices.

Various embodiments also relate to apparatus or systems for performingthese operations. This apparatus may be specially constructed for therequired purpose or it may comprise a general purpose computer asselectively activated or reconfigured by a computer program stored inthe computer. The procedures presented herein are not inherently relatedto a particular computer or other apparatus. Various general purposemachines may be used with programs written in accordance with theteachings herein, or it may prove convenient to construct morespecialized apparatus to perform the required method steps. The requiredstructure for a variety of these machines will appear from thedescription given.

FIG. 1 illustrates a block diagram for an imaging system 100. In oneembodiment, the imaging system 100 may comprise one or more components.Although the imaging system 100 shown in FIG. 1 has a limited number ofelements in a certain topology, it may be appreciated that the imagingsystem 100 may include more or less elements in alternate topologies asdesired for a given implementation. The imaging system 100 may include aplurality of modules, including imaging module 102, movement analysismodule 114, and image correction module 116, which may each include oneor more processing units, storage units, network interfaces, or otherhardware and software elements described in more detail herein. In someembodiments, these modules may be included within a single imagingdevice, utilizing shared CPU 120. In other embodiments, one or moremodules may be part of a distributed architecture, an example of whichis described with respect to FIG. 11 .

In an embodiment, each module of imaging system 100 may comprise withoutlimitation an imaging system, mobile computing device, a smart phone, ora desktop computer, or other devices described herein. In variousembodiments, imaging system 100 may comprise or implement multiplecomponents or modules. As used herein the terms “component” and “module”are intended to refer to computer-related entities, comprising eitherhardware, a combination of hardware and software, software, or softwarein execution. For example, a component and/or module can be implementedas a process running on a processor, such as CPU 120, a hard disk drive,multiple storage drives (of optical and/or magnetic storage medium), anobject, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component and/or module. One or morecomponents and/or modules can reside within a process and/or thread ofexecution, and a component and/or module can be localized on onecomputer and/or distributed between two or more computers as desired fora given implementation. The embodiments are not limited in this context.

The various devices within system 100, and components and/or moduleswithin a device of system 100, may be communicatively coupled viavarious types of communications media as indicated by various lines orarrows. In various embodiments, the various modules and storages ofsystem 100 may be organized as a distributed system. A distributedsystem typically comprises multiple autonomous computers thatcommunicate through a computer network. It is worthy to note thatalthough some embodiments may utilize a distributed system whendescribing various enhanced techniques for data retrieval, it may beappreciated that the enhanced techniques for data retrieval may beimplemented by a single computing device as well. The embodiments arenot limited in this context.

In an embodiment, imaging module 102 may include an imaging source 106and a detector 108, which may be used to perform breast imaging (2D,tomosynthesis, computed tomography, ultrasound or any combinationthereof), and may be an x-ray source and detector in some examples. Inother examples, imaging source 106 and detector 108 may be other typesof imaging sources and sensors, respectively. For example, in someembodiments imaging module 102 may be configured to perform breastimaging, such as x-ray mammography, tomosynthesis, computed tomography,and/or ultrasound. Tomosynthesis is a method for performinghigh-resolution limited-angle tomography at radiographic dose levels.While mammography is used as an exemplary embodiment through thedescription, it can be appreciated that the techniques described hereinmay be applicable to other procedures in which imaging of human tissuesusceptible to movement may occur.

Imaging source 106 may be configured to expose human tissue, such asbreast tissue, to x-rays, which may be detected by detector 108.Detector 108 may be configured to respond to the influence of incidentx-rays over a wide range. Detector 108 may be configured to absorbx-rays, produce an electronic signal, digitize the signal, and store theresults in one of storage 104 and/or database 122. The output image maybe saved as a two-dimensional matrix, where each element represents thex-ray transmission corresponding to a path through the breast tissue.Three-dimensional images and matrices may be generated in someembodiments, depending on the imaging modality, such as tomosynthesis,computed tomography, and the like. The image may be digitally processedsuch that when it is displayed on a display device or printed on laserfilm, it will illustrate the key features required for diagnosis. Suchdiagnostic images may be stored in storage 104 so that they may beviewed on a user interface of display 124.

In an embodiment, images may also be archived in image database 122. Inthis manner, patient records may be maintained and past images may beused to evaluate detected movement when compared to new images. In anexemplary embodiment, an image correction module, described herein, mayrefer to archived images containing common elements (e.g., stillcalcification for the same tissue of the same patient) and compare to acurrent image (which may include blurry calcifications for the sametissue of the same patient). Such as analysis, combined with thetechniques described herein, may be used to detect and/or correct motionartifacts within an image.

Imaging system 100 may include a force sensor 110, which may becontained within a compression paddle of imaging system 100 (not shownin FIG. 1 , illustrated in FIGS. 2 and 3 ). Force sensor 110 may includea strain gauge, piezoelectric sensor, load cell, or other sensor capableof measuring the force applied to human tissue compressed between acompression paddle and an opposite detector plane. In some embodiments,force sensor 110 may include an analog filter, gain circuits for signalconditioning, and/or an analog-to-digital converter for signal capture.The output of force sensor 110 may be an electrical signalrepresentative of a force level. The force level may represent ameasurement of force applied superior to the breast via the compressionpaddle and/or via the imaging detector “top” surface. The electricalsignal representative of a force level may be filtered or converted byone or more circuits or modules described herein into a value thatindicates movement. This movement signal, when compared to othermeasurements over time, may indicate movement of the patient undergoingan imaging procedure.

Imaging system 100 may include a movement detection circuit 112,configured to receive an electronic force signal from force sensor 110and filter a movement signal from the received force signal In someembodiments, the received force signal may include a low frequencycompression force signal (e.g., 0 (DC) to <5 Hz), which may be tappedand processed in parallel using movement detection circuit 112. Movementdetection circuit 112 may include one or more components to process andfilter the force signal, including a DC signal block, such as a blockingcapacitor to remove the DC and low frequency components of the forcesignal, leaving a higher frequency (AC) component, referred to herein asa movement signal One or more analog circuits may filter and apply gainto the higher frequency (AC) signal components to improvesignal-to-noise ratio, if needed. The resulting movement signal mayinclude motion artifacts from the original force signal. As describedlater, one or more modules, such as movement analysis module 114 mayinclude a digital processing unit and corresponding software to analyzethe output from movement detection circuit 112.

In an embodiment, a movement analysis module 114 may include one or moreanalog circuits, such as a tuned differentiator, to detect movement ofhuman tissue compressed within imaging system 100 using a receivedmovement signal from movement detection circuit 112. In someembodiments, movement analysis module 114 may include hardware and/orsoftware modules configured to accept the movement signal from movementdetection circuit 112, and detect tissue movement caused by the patient.An exemplary logic flow illustrating movement detection by movementanalysis module 114 is set forth within FIG. 4 . By way of example andnot limitation, movement may be caused by respiratory activity, cardiacactivity, or muscular movements (voluntary or involuntary) by thepatient. Movement analysis module 114 may be configured with a movementthreshold value, beyond which, movement of the patient is detected andcommunicated to an image correction module 116.

Image correction module 116 may be configured to receive a determinationfrom movement analysis module 114 that movement has been detected. Thedetermination may include data indicating a movement time and movementlevel in some embodiments, and the determination may be used todetermine a corrective action to be taken. Techniques described hereinstrive to improve image quality, even in situations where movement isdetected, reduce patient radiation exposure when possible, and reducethe time required for patients to undergo imaging procedures. Exemplarycorrective actions are described herein with respect to FIGS. 5, 7, and8 however, other corrective action may be taken consistent with thesegoals, in some embodiments.

A database of movement criteria 118 may be used by image correctionmodule 116 to determine the proper corrective action based upon variousdeterminations by movement analysis module 114. For example, criteriawithin movement criteria database 8 may include movement thresholds,time thresholds for delay, image quality criteria, thresholds indicatingthe maximum number of images that can be deleted from an image sequencedue to detected movement, and other criteria necessary to determine andtake corrective actions. In an example, image correction module 116 mayinclude hardware and/or software configured consistent with thetechniques described herein to take one or more corrective actions whenmovement exceeding a threshold has been detected. As described furtherwith respect to FIG. 5 , certain movement determinations may be handledin different ways. In an embodiment, image improvements may be made bydeleting images associated with movement above a threshold. In anembodiment, an image capture procedure may be delayed until detectedmovement has fallen below a threshold. In an embodiment, an imagecapture procedure may be extended so that a proper exposure can be takenwhile also excluding images from an imaging sequence impacted bymovement. In an embodiment, an image capture procedure may be canceled,reducing patient radiation exposure.

In some embodiments, artifact-based image detection of patient motion asdescribed in the '180 Patent, may be combined with the information fromthe force sensor 110 and the movement detection circuit 112 in themovement analysis module 114. In one example, the movement analysismodule 114 may correlate the information received from the motiondetection circuit with the artifact based image detection.

In an embodiment, display device 121 may include a user interfaceconfigured to receive and display an image along with information withrespect to detected movement and any corrective actions taken inresponse. In an embodiment, display 124 may be configured to display analert or movement score (FIGS. 7 and 8 ) indicating to a practitionerthat movement was detected and/or a level of detected movement.Optionally, imaging system 100 may include an indicator 126, which mayinclude an LED, that may be triggered when movement exceeding athreshold has been detected during a procedure. In addition to anotification via the user interface of display 124 or optional indicator126, other techniques for notification of detected movement may be used.Non-limiting examples include audio notification, haptic notification,other visual indication using lights, and/or one or more prompts withinthe user interface.

FIG. 2 illustrates an imaging system 200 according to an embodiment.Imaging system 200 illustrates exemplary components most relevant to thetechniques described herein and may include other components notdepicted within FIG. 2 . Upper portion 202 including imaging source 204,which may he an x-ray source in some embodiments and may be consistentwith imaging source 106, described above with respect to FIG. 1 .

Compression paddle 206 may be mounted to an arm, itself connected to aframe connected to a body of the imaging system 200. Compression paddle206 may be lowered onto human tissue during an imaging procedure.Certain imaging procedures, such as mammography, may require compressionof human tissue between compression paddle 206 and another surface, suchas the surface of detector 214, which may be consistent with detector108, described above with respect to FIG. 1 .

Force sensor module 208 may be contained within compression paddle 206,and may detect force 212 imparted on breast 210, which is placed betweencompression paddle 206 and imaging detector 214. The detected force mayrepresent a measurement of force applied superior to the breast via thecompression paddle 206 and/or via the imaging detector 214 “top”surface. Additionally or separately, a force sensor module may beincorporated into the imaging detector 214 component. In thisconfiguration, the force sensor module incorporated into the imagingdetector 214 may operate in the same manner as the force sensor module208 and may measure the DC and AC compression signals applied by thecompression paddle 206 upon the human tissue (breast 210) that is placedbetween the compression paddle 206 and upon the surface of the imagingdetector 214. As set forth above, force sensor 208, or the optionalforce sensor incorporated into the imaging detector 214, may include astrain gauge, piezoelectric sensor, load cell, or other sensor capableof measuring the force applied to human tissue compressed between acompression paddle and an opposite detector plane, in some embodiments,force sensor 208, or the optional force sensor incorporated into theimaging detector 214, may include an analog filter, gain circuits forsignal conditioning, and/or an analog-to-digital converter for signalcapture. The output of force sensor 208, or the optional force sensorincorporated into the imaging detector 214, may be an electrical signalrepresentative of a force level, which may be filtered or converted byone or more circuits or modules described herein into a value thatindicates movement. This movement signal, when compared to othermeasurements over time, may indicate movement of the patient undergoingan imaging procedure.

In an embodiment, the described force sensor modules may include one ormore circuitry components comprising a movement detection circuit, suchas movement detection circuit 112. In an embodiment, movement detectioncircuit 216 may be implemented separate from force sensor 208, and mayreceive a signal therefrom. As described with respect to FIG. 1 ,movement detection circuit 216 may receive a force signal from forcesensor 208 and filter a high-frequency AC component from the receivedforce signal into a movement signal indicating movement of the humantissue compressed between compression paddle 206 and a surface ofdetector 214.

Movement analysis module 218, which may be implemented in hardwareand/or software, may be configured to determine whether a receivedmovement signal has exceeded a movement threshold. In some embodiments,the movement analysis module 2 8 may be present separate from forcesensor 208, and may be within, the optional force sensor incorporatedinto the imaging detector 214, compression paddle 206 or within anotherportion of imaging system 200, as illustrated. If a movement thresholdhas been exceeded, movement analysis module may communicate thatdetermination to image correction module 220, which may be configured totake corrective action, as described herein with respect to FIGS. 5, 7,and 8 .

FIG. 3 illustrates an imaging system 200 according to an embodiment.Elements within FIG. 3 may be similar to like-numbered elements fromFIG. 2 . The key difference between FIG. 2 and FIG. 3 is theillustration of movement of breast 310. As illustrated, breast 310 maybe moved while between compression paddle 306 and a surface of detector314. This movement may affect a force measurement 312 made by forcesensor 308. While a generally up and down movement is illustrated withinFIG. 3 , it can be appreciated that a variety of movements may be madeby breast 310. Movement may be due to a variety of factors, such asrelating to cardiac or respiratory movements, sneezing, or voluntarilyor involuntarily moving one or more portions of the body that affect themovement of breast 310. As described below, movement of breast 310 maybe of any number of types, and may be temporally evaluated by one ormore modules of imaging system 300. Evaluation of movement type andmovement timing using techniques described herein may provide increasedimage quality and patient experience while reducing patient exposure toradiation.

As discussed above, patient motion during a breast imaging procedure canadversely affect imaging quality and therefore the diagnostic value ofthe resultant images. Detecting and/or measuring motion and correction,however, is difficult due at least in part to the fact that the breastis a non-rigid object. Accordingly, motion patterns of the breast duringthe imaging procedure may be complex in both time and space. Forinstance, some portions of the breast may move differently from otherportions. As a result, image quality may change for different regions ofa breast image. For a modality such as tomosynthesis, the motion ormovement may occur between acquiring projections and/or during exposureof one or more of the projections.

Proper compression and positioning of the breast during the imagingprocedure also affects image quality. Inadequate compression of thebreast may increase the likelihood of unwanted results. For example,inadequate compression may increase the likelihood of motion, whichreduces image quality. As another example, inadequate compression mayincrease the likelihood of overlapping tissue which may make it moredifficult to detect cancerous lesions in a resultant image. Thus, thereis a need to more accurately detect motion and compression in space andtime, which can prove to be useful input data to help correct andenhance image quality during breast imaging procedures.

FIG. 14A illustrates another embodiment of an imaging system 400 whereone or more sensors, in combination or alternatively to the force sensor208, are used. Elements within FIG. 14A may be similar to like-numberedelements from FIG. 1 , FIG. 2 , and/or FIG. 3 . Imaging system 1400illustrates exemplary components most relevant to the techniquesdescribed herein and may include other components not depicted with FIG.14 . The imaging system 1400 includes a compression paddle 1406 and adetector 1414 disposed a distance away from and parallel to thecompression paddle. A breast is compressed between the compressionpaddle 1406 and the detector 1414. While referred to herein as thedetector 1414, the detector 1414 may be considered the housingsurrounding the detector, such as a breast platform. Accordingly, insome examples, discussion of the detector 1414 may be synonymous withdiscussion of the breast platform or the structure housing orsurrounding the actual electronics that detect x-ray beams passingthrough the breast.

One or more sensors 1408 are disposed on or within the compressionpaddle 1406 and the detector 1414. The one or more sensors 1408 maycomprise or communicate with a sensor module which may detect motion ofthe breast and may also be used to detect or analyze compression andpositioning of the breast. In one example, the sensors 1408 may includeone or more photo sensors, infrared sensors and/or ultrasound orultrasonic sensors. The motion detected by the sensors 1408 may be basedon reflected sonic signals and/or reflected light signals depending onthe types of sensors 1408 implemented. For example, the photo sensorsmay include cameras to capture optical images of the breast when it isin a compressed and/or uncompressed state. Similarly, the infraredsensors may be utilized to produce a three-dimensional image or depthmap of the breast that may be used to determine the three-dimensionallocation of exterior of the breast at different points in time. Theultrasound or ultrasonic sensors may also be used to detect thethree-dimensional location of the exterior of the breast. In someexamples, the ultrasound or ultrasonic sensors may also be utilized toimage the interior of the breast. With the interior of the breastimaged, landmarks within the breast may be identified and the locationsof those landmarks may be tracked in three-dimensional space atdifferent points in time.

In some embodiments, the sensors 1408 may be placed in a grid pattern onor within the compression paddle 1406 and the detector 1414. In otherexamples, the sensors 1408 may be disposed around the periphery of thecompression paddle 1406 and the detector 1414. The location and patternof the sensors may be based on the types of sensor and the physicalproperties of the compression paddle 1406 and/or the detector 1414. Forexample, if the compression paddle 1406 is optically opaque, the photosensors may be placed in a position where they have a line of sight tothe exterior of the breast that is not blocked by the compression paddle1406. Similarly, for some ultrasound or ultrasonic sensors, an air gapbetween the sensor and the breast may be undesirable. As such, theultrasonic sensors may be placed in location where there is no air gapbetween the ultrasonic sensor and the breast. Other solid surfaces, suchas a portion of the compression paddle 1406 and/or detector 1414 maystill be located between the ultrasonic sensor and the compressedbreast.

By disposing multiple sensors in a pattern, a more detailedunderstanding of motion of the breast may be obtained. It is appreciatedthat movement of the breast may not be uniform. For example, some areasof the breast may move more than others. Use of multiple sensors allowsthe imaging system 1400 to create a motion map that may be capable ofvisually showing the location of movement throughout the surface of thebreast. In other examples, the motion map may not be a visualrepresentation but rather a set of data indicating the locations of thebreast that moved as well as the magnitude and direction of the breastmovement at each location. For instance, the motion map may be a set ofmotion vectors for different positions in three-dimensional space. Byhaving a more complete understanding of the location of motion of thebreast, the imaging system can determine whether the motion may have hada negative effect on the image obtained. In addition, having additionalsensors allows the imaging system to obtain other information such asthe amount of contact with the breast, as further discussed below, todetermine breast positioning and compression information.

The sensors 1408 that may be incorporated into the imaging detector 1414and/or the compression paddle 1406 may include an analog filter, gaincircuits for signal conditioning, and/or an analog-to-digital converterfor signal capture. The output of sensors 1408 may be electrical signalsrepresentative of motion and/or spatial data representative of locationof the breast, which may be filtered or converted by one or morecircuits or modules described herein into a plurality of spatialinformation or data 1416. The spatial information may be combined tocreate a motion map 1418 a. The motion map 1418 a takes spatialinformation from each of the sensors 1408 to create a relativerepresentation of motion. The motion map 1408 a may describe some areasof the breast that include more motion than others. The motion map 1408a may be a visual representation of the spatial information having somecolors (e.g. red) represent higher amount of motion and other colorsrepresent moderate (e.g. yellow) or low (e.g. green) amount of motion.The relative representation of motion may be determined based on spatialinformation comparison to a threshold or a look up table representingvarious levels of motion. In other examples, the motion map 1418 a maynot include a visual representation but rather a set of data indicatingthe locations of the breast that moved as well as the magnitude anddirection of the breast movement at each location. For instance, themotion map 1418 a may be a set of motion vectors for different positionsin three-dimensional space.

In addition, the motion map 1408 a may be created for each of thetomosynthesis projections or slices created. For example, FIG. 14A showstwo tomosynthesis projections. One projection has a larger degree ofmotion and another projection showing smaller degree of motion. It isappreciated that any number of motion maps 1408 a may be created basedon the number of projections. As an example, spatial data representativeof the breast may be captured by the sensors 1408 during different timesduring the imaging procedure. For instance, the spatial data may becaptured at substantially the same time as when a projection iscaptured. The spatial data may also be captured by the sensors 1408continuously or substantially continuously. Accordingly, a change inposition or location of the breast (or a portion thereof) between thecapture of the projections may be determined. Such a change in positionor location may be indicative of motion of the breast (or a portionthereof). Based on the magnitude of the motion, particular projectionsmay be discarded when generating a tomosynthesis reconstruction of thebreast. In other examples, the projections may be corrected prior to orduring generation of the tomosynthesis reconstruction of the breast.

The information or data from the motion map 1418 a may be provided asinput into an image correction module or algorithm 1420 a. In someexamples, the motion map 1418 a may be utilized to generate a correctionmap. That correction map may effectively be an inverse of the motion map1418 a. For instance, the correction map may indicate how pixels in theimage should be adjusted based on the detected motion. The imagecorrection module or algorithm 1420 a may be similar to the imagecorrection modules 116 and 220 described above with respect to FIGS. 1,2 , and 3, and may perform the functions and correction as furtherdescribed with reference to FIGS. 4 and 5 . The image correctionalgorithm 1420 a may then output corrected images 1420 a.

The spatial information 1416 may also be used to create a contact map1418 b. An example of a contact map 1418 b is depicted in FIG. 14B. Itis appreciated by inventors that the entirety of the breast 1410 is notin contact with the compression paddle 1406 and the detector 1414 whenthe breast is compressed. For example, when the breast is compressedbetween the paddle 1406 and the detector 1414, a portion of breast nearthe periphery of the breast is not in contact with either the paddle1406 or the detector 1414. There may be a line 1432, referred to as the“uncompressed tissue line” or the “paddle contact line” in an image,which defines a contour of contact points of breast withpaddle/detector. The contact map 1418 b may also display the breastprofile or skin line 1430 of the image of the breast. The location ofuncompressed tissue line 1432 with respect to breast profile or skinline 1430 may also be used to give a metric of the adequacy of thecompression and/or positioning of the compressed breast. For example,the larger the area of uncompressed tissue, the less adequate thecompression. It is further appreciated by the inventors that a less thanadequate level of compression may result in poor image quality.

The contact map 1418 b shows or indicates the level of contact with thebreast. The contact map 1418 b can be used to determine or define aroll-off region 1434, which is the region where the breast isuncompressed. The roll-off region 1434 may be the area between theuncompressed tissue line 1432 and the skin line 1430 of the breast. Thesize of the roll-off region 1434 may be represented by the area betweenthe uncompressed tissue line 1432 and the skin line 1430. The size ofthe roll-off region 1434 may also be represented by a distance betweenthe uncompressed tissue line 1432 and the skin line 1430. The distancemay be the maximum, minimum, and/or average distance between theuncompressed tissue line 1432 and the skin line 1430. The location ofthe uncompressed tissue line 1342 and the size of the roll off region1434 may also be useful in special image processing techniques inuncompressed versus compressed breast areas. The location ofuncompressed tissue line 1432 with respect to breast profile 1430 mayalso be used to give an idea of how adequate the compression is, whichmay also be used in determining the adequacy of positioning of thebreast.

Additional positioning information of the breast may also be determinedfrom the data produced by the sensors 1408. For example, in amediolateral oblique (MLO) compression, the sensors may be used todetermine whether the pectoral muscle is properly positioned such thatit will be imaged during the imaging procedure. The spatial dataproduced by the sensors 1408 may also be used to assess the alignment ofthe nipple, such as by determining the posterior nipple line (PNL).Other positioning criteria may also be determined from the spatial dataproduced by the sensors 1408.

Returning to FIG. 14A, the information from the contact map 1418 band/or the other positioning information may be input in a compressionadequacy analysis module or algorithm 1420 b. The compression adequacyanalysis module 1420 b may be similar to the image correction modules116 and 220, with the difference of that a threshold of compression isused, rather than a motion threshold, to compare the current compressioncontours to the threshold of compression contours. The compressionadequacy analysis module 1420 b may perform at least some of thefunctions and corrections as further described with reference to FIGS.4, 5, and 15A-C. As an example, the compression adequacy analysis mayinclude comparing the area for the roll-off region to a threshold forthe area of the roll-off region. For example, if current compressioncontours are below a threshold for the compression contours, imagecapture may be delayed until contours are above the threshold, or imagecapture may be cancelled if the delay exceeds a threshold. In at leastone example, one or more alerts or alarms as discussed above may begenerated to notify the technologist that compression is inadequate andthe patient may need to be repositioned. Accordingly, determinationsregarding whether compression and/or positioning of the breast areproper may be made prior to the patient being exposed to a dose of x-rayradiation.

Included herein is a set of flow charts representative of exemplarymethodologies for performing novel aspects of the disclosedarchitecture. While, for purposes of simplicity of explanation, the oneor more methodologies shown herein, for example, in the form of a flowchart or flow diagram, are shown and described as a series of acts, itis to be understood and appreciated that the methodologies are notlimited by the order of acts, as some acts may, in accordance therewith,occur in a different order and/or concurrently with other acts from thatshown and described herein. For example, those skilled in the art willunderstand and appreciate that a methodology could alternatively berepresented as a series of interrelated states or events, such as in astate diagram. Moreover, not all acts illustrated in a methodology maybe required for a novel implementation.

FIGS. 15A-C illustrate a logic flow 1500 according to an embodiment. Thelogic flow 1500 may be representative of some the operations executed byone or more embodiments described herein, such as imaging system 100,for example. At operation 1502, a first set of spatial data of thebreast is generated at a first time point based on data captured by thesensors that may be incorporated into at least one of the breastcompression paddle and/or the imaging detector. At operation 1504, asecond set of spatial data of the breast is generated at a second timepoint based on data captured by the sensors. The spatial data of thebreast at each time point may be representative of the location of thebreast at the respective time point. The spatial data may bethree-dimensional data about the exterior and/or interior of the breast.The time points may also coincide with the acquisitions of projectionsin tomosynthesis imaging procedure as well as time points prior to anyx-ray exposures during the imaging procedure. For example, the firsttime point may be after compression but prior to the first exposure. Thesecond time point may coincide with the first projection. While only twotime points are depicted in logic flow 1500, it should be appreciatedthat additional spatial data may be captured or generated at additionaltime points. For example, spatial data may be generated at a third timepoint coinciding with the capture of a second projection. The spatialdata may also be continuously or substantially continuously generatedand captured during the imaging procedure.

At operation 1506, motion data may be generated based on the first setof spatial data and the second set of spatial data. For example, bycomparing the first spatial data to the second spatial data, adifference in location of the breast or portions of the breast may bedetermined. That change in location corresponds to motion. The generatedmotion data may indicate an amount of motion that occurred for thebreast or a portion of the breast. Generating motion data may alsoinclude generating a motion map at operation 1508. The motion map, forexample, may be a visual representation of the spatial informationhaving some colors represent higher amount of motion and other colorsrepresent moderate or low amount of motion. At operation 1510, acorrection map may be generated from or based on the motion map. Thatcorrection map may effectively be an inverse of the motion map. Forinstance, the correction map may indicate how pixels in the image shouldbe adjusted based on the detected motion.

The motion data generated in operation 1506 may also be utilized formotion correction or tomosynthesis reconstruction, as shown in FIG. 15B.At operation 1518, the motion data may be provided as an input into amotion correction algorithm. For example, the motion map and/or thecorrection map may be provided to the motion correction algorithm. Themotion correction algorithm then corrects at least one medical imagebased on the input of the motion data to generate one or more correctedmedical images in operation 1520. For example, if the second spatialdata is generated for a time point that coincides with the capture of amedical image, the motion data may be used to correct that medicalimage. Accordingly, the image correction algorithm may correct a medicalimage acquired at substantially the second time point. Using atomosynthesis procedure as an example, the first spatial data and afirst projection may be captured at the first time point and the secondspatial data and a second projection may be captured at the second timepoint. The motion data may then be used to correct the second projectionprior to the projections being used to generate a tomosynthesisreconstruction at operation 1528. While only two projections arediscussed in the example, it should be understood that such correctionsmay be applied to any of number of projections captured during atomosynthesis imaging procedure.

The motion data for each of the projections or medical images may alsobe compared to a predetermined motion threshold at operation 1522. Forexample, an amount of motion that occurred between a first projectionand a second projection may be compared to a motion threshold. If theamount of motion that occurred between the projections is greater than athreshold, the projection or medical image may be discarded at operation1526. For example, the second projection may be discarded if the amountof motion that occurred between the first and second projection isgreater than the predetermined motion threshold. The thresholdsdescribed herein may be dynamic or predetermined. For example, thethresholds may be dynamically determined by an imaging system during theimage capture process based, at least in part, on a detected imagequality assessment taken in near real-time. In other embodiments, amovement threshold may be predetermined and stored within an imagingsystem. The predetermined thresholds discussed herein may be a settingor a value that is stored or accessed by the medical imaging system or aportion thereof. For instance, the predetermined thresholds may be setby a medical professional, be provided with the imaging system, oraccessed from a remote source. The thresholds may be based on values,percentages, ratios, or other types of thresholds.

If, however, the motion amount is not greater than the predeterminedthreshold, the projection or image is retained at operation 1524. Theimages or projections that are retained may then be used to generate atomosynthesis reconstruction in operation 1528. In some examples, imagesthat are discarded in operation 1526 may be regenerated through asynthesis or interpolation of other acquired projections. Thosesynthesized projections may then be used in generating the tomosynthesisreconstruction in operation 1528.

Returning to FIG. 15A, the spatial data captured in operation 1502and/or operation 1504 may also be utilized to generate compression andposition data in operation 1512. For example, the first spatial datacaptured in operation 1502 may be captured when the breast is compressedbut prior to an x-ray exposure having occurred. Compression and/orposition data may be generated for the breast that corresponds to thattime of compression at operation 1512. Generating compression data mayinclude generating a contact map for the compressed breast in operation1514. Generation of the contract map may also include determining orgenerating values representative of the roll-off region. For example, avalue for the area of the roll-off region or a distance representativeof the roll-off region may be determined. The value for the roll-offregion may be at least one of an area of the roll-off region, a maximumdistance between the uncompressed breast line and the skin line, aminimum distance between the uncompressed breast line and the skin line,or a ratio between the area of the roll-off region and an area of thebreast in contact with the at least one of the breast compression paddleor the imaging detector, among other possible values. Generatingposition information may include generating positional values for thebreast at operation 1516. The positional values may include values forpositioning metrics, such as a value for the posterior nipple line (PNL)or another value relating to the position of the pectoral muscle. Valuesfor other positioning metrics may also be generated from the spatialdata.

As shown in FIG. 15C, at operation 1530, the compression and/or positiondata generated in operations 1512-1516 may be compared to one or morethreshold values. For example, with respect to compression, a value forthe roll-off region of the breast may be compared to a predeterminedthreshold for that value. As an example relating to positioning data, avalue for the PNL may be compared to a predetermined threshold value forthe PNL. If the compression values and/or the positioning values do notexceed the thresholds (or are otherwise within a tolerance threshold),then imaging continues and the medical images, such as mammography ortomography images are acquired at operation 1532.

If the compression values and/or the positioning values do exceed thethresholds (or are outside of the tolerance thresholds), then anotification or alert may be generated in operation 1534. Thenotification may be a visual notification, such as a notificationdisplayed on a screen or indicated by illumination of a light. Thenotification may also be an audible notification played through aspeaker or other sound-making device. The notification may indicate thatthe compression is inadequate or improper and/or that the breast inimproperly positioned. The notification may further indicate a reason asto why the compression was inadequate, such as too large of a roll-offarea, and/or why the breast was improperly positioned, such as animproper PNL value. In addition, the notification may provide guidanceto the medical professional or technician as to how the breast should berepositioned. At operation 1536, the breast may be repositioned orrecompressed. Once the breast is repositioned or recompressed, theimaging procedure continues and medical images are acquired at operation1532. In some examples, upon repositioning and/or recompressing thebreast at operation 1536, method 1500 flows back to the start wherespatial data is recaptured at operation 1502. The operations determiningwhether the positioning and/or compression is proper may then berepeated for the repositioned and/or recompressed breast until thebreast is determined to be in a proper position and properly compressed.

FIG. 4 illustrates a logic flow 400 according to an embodiment. Thelogic flow 400 may be representative of some or all of the operationsexecuted by one or more embodiments described herein, such as imagingsystem 100, for example. Specifically, logic flow 400 may illustrateoperations performed by a movement analysis module, such as movementanalysis module 114.

At 402, a movement analysis module may receive a movement signal from aforce sensor and/or movement detection circuit. The movement signal mayinclude motion artifacts indicating that human tissue, currently undercompression during an imaging procedure, has moved. Using hardwareand/or software components, the received movement signal may beevaluated to isolate data indicating movement and a value may beassigned indicating a movement level. In an embodiment, a baselinemovement signal may be first evaluated, indicating a baseline movementvalue, or a baseline movement value may be stored within an imagingsystem. Subsequent movement signals may be received and compared to thebaseline movement value to identify motion artifacts within thesubsequent movement signals.

At 404, the movement analysis module may compare subsequently receivedmovement signals, and any motion artifacts identified therein, to amovement threshold, which may be predetermined and stored within anon-transitory computer-readable storage medium. In some embodiments,thresholds may be dynamically determined by an imaging system during theimage capture process based, at least in part, on a detected imagequality assessment taken in near real-time. In other embodiments, amovement threshold may be predetermined and stored within an imagingsystem.

At 406, the movement analysis module may determine whether the receivedmovement signal has exceeded the movement threshold and, at 408, themovement analysis module may communicate the determination to an imagecorrection module, which is discussed in more detail below. Thedetermination, in some embodiments, may include an indication thatmovement has been detected, a movement value, a timestamp, a frameidentifier, or other information that may be necessary for an imagecorrection module to take appropriate corrective measures based upon thedetected movement.

FIG. 5 illustrates a logic flow 500 according to an embodiment. Thelogic flow 500 may be representative of some or all of the operationsexecuted by one or more embodiments described herein, such as imagingsystem 100, for example. Specifically, logic flow 500 may illustrateoperations performed by an image correction module, such as imagecorrection module 116.

At 502, an image correction module may receive a determination thatmovement has been detected. In some embodiments, any movement may becommunicated to the image correction module. In other embodiments, onlymovement that exceeds a threshold, as described herein, may becommunicated to the image correction module. The determination, in someembodiments, may include an indication that movement has been detected,a movement value, a timestamp, a frame identifier, or other informationthat may be necessary for an image correction module to take appropriatecorrective measures based upon the detected movement.

At 504, the image correction module may determine a type of movementbased upon one or more received movement determinations. For example, amovement may be categorized as a regular movement when it is repetitiveand generally within a regular time interval. This type of movement mayindicate a patient is breathing, or moving in a regular fashion. Inanother example, movement may be categorized as irregular. A singleirregular movement may indicate a patient has shifted positions, orsneezed, for example. In yet another example, movement may becategorized as continuously irregular. A determination of movement typemay be based, in part, on a movement value and/or timestamp, forexample. In at least one example, a determination of the movement may bethat the movement is localized to one or more tomosynthesis slices.

At 506, when a regular movement that is repetitive and generally withina regular time interval is detected, the image correction module mayconfigure the image capture to the synchronized with the regularmovement. In this manner, image capture may be performed during a timeperiod in which movement is not detected, and skipped during a timeperiod in which movement is detected. The synchronized image sequencemay be generated at 512, and may include only images in which movementhas not been detected, or detected movement is below a threshold amount.

At 508, when irregular movement is detected, the image correction modulemay delay image capture for a period of time, allowing the movement tostop so an image is not negatively impacted. As described herein, someembodiments may flag image captured images taken during a movement, andthose images may be removed from an imaging sequence used to generate animage.

At 510, if a termination of movement is localized to one or moretomosynthesis slices. The slices may be removed or cancelled from thetomosynthesis stack that are associated with movement above a threshold.

At 514, if irregular movements continue during the delay period, thedelay may be extended until movement stops. However, since in some casesthe patient may be exposed to x-ray radiation during the delay, a timeperiod threshold may be set for which the image capture may be canceledif the delay lasts beyond the threshold. Thus, an image may be generatedat 512 if the delay is within the time threshold, and the image capturemay be canceled at 516 if the delay period extends beyond the timethreshold. In this manner, an imaging system may be able to compensatefor some movement, and generate higher quality images by delayingcapture until movement is no longer detected, while at the same timecanceling an image and limiting patient radiation exposure when asatisfactory image cannot be obtained due to excessive irregularmovement.

During image generation at 512, certain embodiments may correlate imageswith detected movement and flag images in which movement was detected.In this manner, images flagged with movement may be removed from aresulting imaging sequence, thus, improving overall image qualitydespite detecting motion within the imaging procedure. In some cases,many images may be flagged as occurring during movement and the entireimaging sequence may need to be canceled. Based upon a particularprocedure, for example, a threshold may be set such that an imagecorrection module may determine whether the process of deleting imagesmay result in a usable image sequence, or if the imaging sequence needsto be canceled due to excessive movement during the imaging procedure.

FIG. 6 illustrates a logic flow 600 according to an embodiment. Thelogic flow 600 may be representative of some or all of the operationsexecuted by one or more embodiments described herein, such as imagingsystems 100, 200, and/or 300, for example. At 602, a force sensor maygenerate a force signal indicating a measure of force applied to humantissue being compressed between a compression paddle and an imagingdetector to capture an image of the human tissue. As set forth above, aforce sensor may include a strain gauge, piezoelectric sensor, loadcell, or other sensor capable of measuring the force applied to humantissue compressed between a compression paddle and an opposite detectorplane. In some embodiments, a force sensor may include an analog filter,gain circuits for signal conditioning, and/or an analog-to-digitalconverter for signal capture. The output of a force sensor may be anelectrical signal representative of a force level, which may be filteredor converted by one or more circuits or modules described herein into avalue that indicates movement. This movement signal, when compared toother measurements over time, may indicate movement of the patientundergoing an imaging procedure.

At 604, a movement detection circuit may filter the received forcesignal and isolate a movement signal from therein. The movement signalmay indicate a level of force, and in some cases may indicate that apatient has moved during image capture in a manner that is detrimentalto the quality of a resulting image. As set forth above, the movementdetection circuit may be configured to receive an electronic forcesignal from a force sensor and filter a movement signal from thereceived force signal. In some embodiments, the received force signalmay include a low frequency compression force signal (e.g., 0 (DC) to <5Hz), winch may be tapped and processed in parallel using the movementdetection circuit. Further, the movement detection circuit may includeone or more components to process the force signal, including a DCsignal block, such as a blocking capacitor to remove the DC and lowfrequency components of the force signal, leaving a higher frequency(AC) component, referred to herein as a movement signal. One or moreanalog circuits may filter and apply gain to the higher frequency (AC)signal components to improve signal-to-noise ratio, if needed. Theresulting movement signal may include motion artifacts from the originalforce signal.

At 606, a movement analysis module may determine whether a detectedmovement is beyond a movement threshold. The movement analysis modulemay include one or more analog circuits, such as a tuned differentiator,to detect movement of human tissue compressed within an imaging systemusing a received movement signal from the movement detection circuit. Insome embodiments, the movement analysis module may include hardwareand/or software modules configured to accept the movement signal fromthe movement detection circuit, and detect tissue movement caused by thepatient. An exemplary logic flow illustrating movement detection by amovement analysis module is set forth within FIG. 4 . By way of exampleand not limitation, movement may be caused by respiratory activity,cardiac activity, or muscular movements (voluntary or involuntary) bythe patient. A movement analysis module may be configured with amovement threshold value, beyond which, movement of the patient isdetected and communicated to an image correction module at 608.

At 608, when movement is beyond a threshold, an image correction modulemay perform a corrective action, which may include one or more of avariety of actions that improve image quality and reduce patientexposure to radiation. An image correction module may be configured toreceive a determination from movement analysis module that movement hasbeen detected. The determination may include data indicating a movementtime and movement level in some embodiments, and the determination maybe used to determine a corrective action to be taken, some of which aredescribed with respect to FIG. 5 , and below with respect to FIGS. 7 and8 . Techniques described herein strive to improve image quality, even insituations where movement is detected, reduce patient radiation exposurewhen possible, and reduce the time required for patients to undergoimaging procedures. While exemplary corrective actions are describedherein, other corrective action may be taken consistent with thesegoals, in some embodiments.

FIG. 7 illustrated a generated image 700 according to an embodiment.Generated image 700 may be generated by one or more imaging systemsdescribed herein, for example, imaging systems 100, 200, and/or 300. Insome embodiments, corrective actions may include visual indicationswithin a graphical user interface of a display during or after animaging procedure, using an indicator of an imaging system, and/or usinga graphical indication on a generated image itself. FIG. 7 illustratesan alert 702, which may be displayed on a display of an imaging system,indicating to a practitioner or patient that movement was detectedduring the imaging procedure. Such an indication may alert those viewingthe image that quality issues may be fixed by reducing motion insubsequent imaging procedures.

FIG. 8A illustrates a generated image 800 according to an embodiment.Generated image 700 may be generated by one or more imaging systemsdescribed herein, for example, imaging systems 100, 200, and/or 300. Insome embodiments, corrective actions may include visual indicationswithin a graphical user interface of a display during or after animaging procedure, using an indicator of an imaging system, and/or usinga graphical indication on a generated image itself. FIG. 8A illustratesan alert 802 indicating a motion score, which may indicate a score on arelative scale of motion detected during an imaging procedure. In anexample, a minimum and maximum level of movement may be stored within anon-transitory computer-readable storage medium of an imaging system.Once movement has been detected, an image correction module may performa calculation of the detected movement and determine a score based uponthe stored minimum and maximum values, in this manner, a practitioner orpatient may be provided with an indication of how much movement wasdetected, and may take steps to improve image quality in subsequentimaging procedures. In another example, the score may be pass/failscore, with pass meaning that the motion is below the threshold and nocorrective action is needed, and fail meaning that corrective action isneeded. Other scoring methodologies are contemplated. In one embodiment,the positioning information from many images can be aggregated intoanalytics and supplied to the facility and other entities for thepurposes of training, education, analytics and compliance.

FIG. 8B shows the positioning information collected and analyzedaccording to one embodiment. Each image may be associated with aradiology technologist or technologist who took the image (e.g. atechnologist identification number) and associated with a patientpositioning score for that image as described above. The information maybe stored in the imaging system 100. The information may then betransmitted to a centralized computer system 1000, such as the system1000 described below with reference to FIG. 10 . The centralizedcomputer system 1000 may part of a cloud-computing system. The scores,the technologist IDs, and other information collected from the imagingsystem 100 may be aggregated over time. The scores for a particulartechnologist or a particular facility may be analyzed, for example, bythe centralized system 1000, to determine if one or more correctiveactions are needed. In one example, a particular technologist's averagescore is compared to average scores of others or other technologists inthat particular facility, or in other facilities. In another example, aparticular facility's average score may be compared to the average scoreof other facilities. If the centralized system determined that thetechnologist's or facility's scores are below a particular threshold,the technologist or members of the facility may be recommended forpatient positioning education or quality control improvements. In oneexample, a look-up table or algorithm determines whether correctiveaction is needed and what type of action to recommend based in part onthe score.

Additional examples of use of the positioning information may includecompliance with Federal Regulations, such as the Mammography QualityStandards Act (MQSA) and the Enhancing Quality Using the InspectionProgram or EQUIP initiative. The MQSA requires that the images taken ata facility must comply with certain quality standards. Poor positioningis a factor in most deficiencies and failures of clinical images to meetquality standards. EQUIP requires regular review of images, correctiveprocedures when clinical images are of poor quality, including amechanism for providing ongoing feedback to technologists or otherdesignated personnel, and oversight of quality control records. Theanalytics described above can be used to generate reports of compliancewith federal regulations. For example, the report may be automaticallygenerated on a periodic basis that includes information such as thescore information for that facility, the number of times correctiveprocedures were taken, the number of times that corrective measures suchas education and quality control measures were recommended and weretaken. Such reports can be stored and provided if needed to federalregulators to ensure compliance. Complying with EQUIP, however, requiresonly annual self-reporting from facilities, which has generally resultedin only a yearly review of imaging quality by facilities. Such a lag inreporting may cause downward trends to go unnoticed and potentially poorimaging procedures to occur. In addition, the metrics may be stored invarious formats across a plurality of devices and imaging systems,making it even more difficult for the metrics to be monitored. Thepresent technology resolves these problems among others by being able tocontinuously aggregate quality metrics across a plurality of facilitiesand provide access and additional insights to those quality metrics insubstantially real time as the quality metrics are aggregated. Thequality metrics may also be provided to the central computing system ina standardized format via the web application to help ensure that thesame types of metrics are properly aggregated and correlated together.Further, due in part to the real time tracking of metrics, warnings andtrainings may be provided based on downward trends in image qualitybefore poor imaging procedures may be implemented by technicians of afacility. Such warnings and trainings may provide for an overallimproved imaging process for facilities and lead to better detection ofabnormalities such as cancers.

FIG. 8C illustrates a logic flow 814 according to an embodiment. Thelogic flow 814 may be representative of some or all of the operationsexecuted by one or more embodiments described herein, such as systems100, 200, 300, and/or 1000, for example. In step 804 information,including one or more scores, is received from one or more technologistsat a first facility. Positioning information, including one or morescores, is received from one or more technologists at a second facility.In step 806, the information may be analyzed and a report is generated.The report may be provided to the facility for which is it associated.In step 810, the information may be compared. In one example, the scoresmay be compared within the facility, for instance, to score thetechnologists relative to each other. In another example, the scores maybe compared to scores at other facilities. In step 812, the scores maybe compared to a threshold to determine if the particular technologistis above or below the threshold. If above, the technologist isadequately performing positioning patient positioning. If below thethreshold, the technologist may require corrective action, for example,education or quality control (QC) corrective actions. In addition, thescores for all the technologists in a particular facility may becompared to a threshold. If the scores for the facility are above thethreshold, a report is generated of compliance to federal regulations.If the scores for the facility are below the threshold, QC for thefacility are recommended. The thresholds may be those types ofpredetermined threshold discussed herein. A report is generated withnon-compliance and recommendations for QC.

FIG. 8D illustrates a logic flow or method 816 for processing medicalimages at a medical facility. The method 816 may be performed by afacility, such as Facility A or B depicted in FIG. 8B. At operation 818,medical images of a patient are acquired. For example, the medicalimages may be acquired by one or more imaging systems 100 located at theparticular facility. The medical images that are acquired may includeadditional identification information stored with the image, such theidentity of the technician that performed the imaging procedure and theidentity of the facility that performed the procedure. Such additionalidentification information may be stored as metadata for the respectivemedical image, in the header of the medical image, or otherwise storedwith the respective medical image such that the additional informationis associated with the respective medical image. The identificationinformation may include the name of the patient, an additional patientidentifier, the date of examination, the view and laterality of theimage, the facility name or identifier and location (e.g., city, state,and zip code of the facility), a technologist identification, acassette/screen identification, and a mammography unit identification(if there is more than one unit in the facility).

At operation 820, a random sampling of the acquired medical images istaken. The random sampling of images helps reduce potential bias infacilities or technicians selecting images that they believe to be ofbetter quality to artificially inflate their scores or metrics. Therandom sampling of images may be of images may be for all medical imagesacquired or of medical images that were actually reviewed byradiologists or other medical professionals. The random sampling may beperformed automatically through an interface or software provided by thecentral computing system to the facility, such as through a webapplication provided by the central computing system. Random samplingcan also be done in real-time (through an algorithm) picking every 3 or4 or 5 patients, based on the prior volumes/seasonality calculations.

At operation 822, quality metrics are then generated from and/or for therandomly sampled images. The metrics may be automatically generatedthrough patient positioning and/or motion detection algorithms, such asthe ones discussed herein and in International Publication No.WO2018/170265, titled “Techniques For Patient Positioning QualityAssurance Prior to Mammographic Image Acquisition,” which isincorporated by reference herein in its entirety. The metrics may alsobe generated by an interpreting physician (IP). For instance, theinterpreting physician may review the medical images that have beenrandomly selected and provide metrics for those medical images. Themetrics from the IP may be input into a web application, interface, orother software provided by the central computing system. The metricsthat are provided may include positioning metrics, compression metrics,exposure level metrics, contrast metrics, sharpness metrics, noisemetrics, and/or artifact metrics, among other metrics. Each metric mayalso include additional sub-metrics or scores. For example, thepositioning metrics may also include sub-metrics such as nipplelocation, nipple angle, pectoral muscle coverage, inframammary foldvisibility, pectoral-nipple line distance, and symmetry between imageviews. The positioning metrics generally relate to whether sufficientbreast tissue is imaged to ensure that cancers or anomalies are notlikely to be missed because of inadequate positioning. The compressionmetrics generally relate to whether compression has been applied in amanner that minimizes the potential obscuring effect of overlying breasttissue and motion artifacts. The exposure level metrics generally relateto whether the exposure level was adequate to visualize breaststructures and whether the images were underexposed or overexposed. Thecontrast metrics generally relate to where the image contrast permitteddifferentiation of subtle tissue density differences. The sharpnessmetrics generally relate to whether the margins of normal breaststructures were distinct and not blurred. The noise metrics generallyrelate to whether noise in the image obscured breast structures orsuggested the appearance of structures not actually present. Theartifacts metrics generally relate to whether artifacts due to lint,processing, scratches, and other factors external to the breast obscuredbreast structures or suggest the appearance of structures not actuallypresent. The quality metrics may further include metrics or scores basedon motion or movement that occurred during imaging the patient. Suchmotion or movement metrics may be generated using the sensors andtechniques discussed herein. The metrics generated at operation 822 maybe stored with the medical image(s) for which the metrics weregenerated.

At operation 824 a lead interpreting physician (LIP) may generateadditional metrics for the medical images for which the metrics weregenerated in operation 822. The LIP may generate metrics for all ofthose images or a subset of those images. The subset of the images maybe a randomized subset of images. The LIP may in some cases modify orconfirm the metrics generated in operation 822. In other examples, theLIP may generate additional metrics for the medical images. Any metricsgenerated by the LIP may also be stored with the medical images. Atoperation 826, the metrics for medical images are sent from the facilityto the central computing system. The medical images may also be sentwith the metrics in some examples. In other examples the metrics and atleast a portion of the identification information may be sent ascorrelated to one another, such as in the same report or otherwiselinked in an exported database.

FIGS. 8E and 8F depict a logic flow or method 828 for processing medicalimage metrics at a central computing system. For example, the method 828may be performed by the central computing system to aggregate andprocess quality metrics from a plurality of facilities. At operation830, a first set of quality metrics for a plurality of medical imagesare received from a first imaging facility, such as Facility A in FIG.8B. The quality metrics received may also include the identificationinformation for the medical images for which the received qualitymetrics correspond. In some examples, the medical images themselves mayalso be received. At operation 832, a second set of quality metrics fora plurality of medical images are received from a second imagingfacility, such as Facility B in FIG. 8B. The quality metrics receivedmay also include the identification information for the medical imagesfor which the received quality metrics correspond. In some examples, themedical images themselves may also be received. While not shown in FIG.8E, method 828 may also include receiving additional quality metricsfrom additional facilities. In addition, the quality metrics receivedfrom the first facility and the second facility may be ongoing. Forexample, the first facility and the second facility may send qualitymetrics at regular intervals, such as monthly or quarterly. When thequality metrics are received by the central computing system, thecentral computing system may continue to store the metrics to tracktrends from different facilities. In some examples, the metrics arereceived through a web application provided to the facilities by thecentral computing system. For instance, the IP and LIP may directlyenter the metrics into the web application, which causes the receipt ofthe metrics by the central computing system. The identificationinformation may also be provided in the web application and/or the webapplication or the central computing system may extract theidentification information from the medical images

At operation 834, the first set of quality metrics are compared to thesecond set of quality metrics. For example, once the first and secondsets of quality metrics are received, the central computing system maycause the two sets of metrics to be compared to one another. In otherexamples where additional metrics are received from additionalfacilities, those metrics may also be compared to one another. In someexamples, individual metrics from the first set may be compared to thecorresponding individual metric of the other set. For instance,positioning metrics for the first facility may be compared topositioning metrics of the second facility. At operation 836, based onthe comparison of the first set of quality metrics to the second set ofquality metrics, a benchmark is set for at least one metric in the firstset of quality metrics and the second set of quality metrics. Thebenchmark may be standard or point of reference against which thequality metrics may be assessed. For example, based on the comparison ofthe aggregated quality metrics from the plurality of facilities, theaverage patient positioning score may be determined. That averagepatient positioning score may then be used as a benchmark for anindividual facility to determine how it is performing as compared tothat benchmark. Benchmarks other than average scores may also bedetermined including benchmarks based on different statistical analysessuch as percentiles. Benchmarks may also be set by government entities,such as the FDA. The benchmarks may also be based on the type offacility. For example, benchmarks may be created for facilities that aresimilarly situated based on factors such as location, number oftechnicians, number of medical images acquired, number of imagingsystems on site, number of interpreting physicians, or othercharacteristics of the facilities. Accordingly, facilities may be ableto compare their own quality metrics against benchmarks that are derivedfrom the like facilities.

At operation 838, a dashboard may be provided by the central computingsystem one or more of the facilities from which quality metrics arereceived. The dashboard may be provided to the facility through the webapplication. Facility data for a facility or a particular technologistmay be generated based on the generated benchmark in operation 836 andthe first set of quality metrics and/or the second set of qualitymetrics. The facility data may include the quality metrics from one ormore facilities compared to the benchmark(s) generated in operation 836.The facility data may be presented in the dashboard. For instance, thefacility data and/or quality metrics for the facility accessing thedashboard may be viewed through the dashboard. For example, an LIP orother member of the facility may access the dashboard to see how thefacility's quality metrics compare to the benchmark quality metricsand/or the quality metrics of other facilities. The quality metrics maybe searched or refined as well through the dashboard. The LIP may refinethe quality metrics based on a period of time or for a certaintechnician or technicians. For instance, if the LIP wanted to see how aparticular technician performed over a certain month, the LIP couldrefine the results in the dashboard to see such information. Reportsregarding the quality metrics of the facility may also be generatedthrough the dashboard. The dashboard may also be used to track how thefacility's quality metrics compare to federal regulations and providewarnings if the metrics are below federal regulations for any timeperiod. Reports may also be generated that indicate how the qualitymetrics of the facility compare to federal regulations or guidelines. Inaddition, a dashboard may also be provided to a government agency orreview board to show how an individual facility is performing or to showhow a group of facilities is performing. The dashboard also providesuseful insights into the quality metrics that were previouslyunavailable. As the metrics are received by the central computingsystem, the dashboard representation for the facility may be updatedalmost immediately. Accordingly, the imaging quality of a facility isable to be tracked over time and in a real time or live manner that hasnever been available before.

The dashboard or the reports may also provide additional insights beyondthe metrics that are reported. For instance, based on the aggregation ofthe quality metrics, a large enough sample of metrics across differentfacilities may allow for correlations between different types of qualitymetrics and the identifying data. Such correlations and insights may begenerated through machine learning techniques. Unsupervised machinelearning techniques may be particularly useful in identifyingcorrelations and insights that may have been previously unknown.Clustering-based, association-based, and anomaly-based unsupervisedlearning algorithms, among others, may all be used for the data.Clustering algorithms are generally directed to problems where the goalis to discover inherent clusters or grouping of data, and associationalgorithms are generally directed to problems where the goal is todiscover rules that describe large portions of data. Anomaly detectionalgorithms generally are directed to discovering unusual or outliermetrics within the set of quality metrics. As an example, the aggregatedmetrics and identification data may be provided as an input to theunsupervised machine learning algorithms to output previously unknownstructures, patterns, and associations within the aggregated metrics andidentification data.

At operation 840, a training recommendation for a facility or aparticular technologist may be generated based on the generatedbenchmark in operation 836. The training recommendation may be generatedby the central computing system. For example, based on a comparison ofthe first set of quality metrics to the generated benchmark, it may bedetermined that the first set of quality metrics (or a subset thereof)are falling short of the benchmark. Based on that determination, atraining recommendation may be provided. As an example, the positioningmetrics for a particular technician may be below a benchmark or afederal regulation or guideline. A training for that technicianregarding positioning may then be generated by operation 840. Thetraining may also be tailored to the specific positioning metrics thatare problematic for the specific technician. The trainings may begenerated from training sets available from different trainingorganizations or custom trainings provided by the company hosting oroperating the central computing system. For instance, the trainings maybe a set of videos for the technician to watch to better understand howto properly position the patient. The video trainings may also includeinteractive elements that further improve the interaction of thetechnician during the training. Such interactive trainings may alsoinclude an assessment following or during the training that assesses howwell the technician is understanding the information provided by thetraining. The generated training may also include recommendations forprograms that are available based on the particular problematic metrics.

At operation 842, the training generated in operation 840 is sent to thefacility. For example, when the training has been generated for atechnician at the first facility, the generated training is sent to thefirst facility. The training may be sent to the first facility by thecentral computing system via the web application or other form ofcommunication. The training may be in the form as a series of videosthat are accessible via the web application by the technician.Similarly, interactive trainings may also be provided via the webapplication. During the interactive trainings, an assessment score maybe stored that indicates how well the technician performed during thetraining. That assessment score may be stored and associated with thattechnician for the particular training that was generated at operation840.

At operation 844 (shown in FIG. 8F), a subsequent set of quality metricsfor a plurality of medical images is received. For example, the centralcomputing system may receive, from the first imaging facility, asubsequent set of quality metrics for a plurality of medical images thatwere acquired after the medical images for which metrics were receivingin operation 830. Subsequent sets of quality metrics may also bereceived from additional facilities as well. As discussed above, thequality metrics may be continuously received from the facilities as thequality metrics are generated. In some examples, the subsequent set ofquality metrics may be received from a facility that has received and/orcompleted the training sent in operation 842.

At operation 846, the subsequent set of quality metrics are compared toquality metrics previously received by one or more of the imagingfacilities. For example, when the subsequent quality metrics arereceived from the first imaging facility, those subsequent qualitymetrics are compared to the first set of quality metrics received inoperation 830. At operation 848, based on the comparison of thesubsequent quality metrics to the prior quality metrics, such as thefirst set of quality metrics, an effectiveness rating for a training isgenerated. The effectiveness rating may be generated for the trainingthat was generated in operation 840 and sent to the facility inoperation 842. The effectiveness rating indicates how effective thetraining was that was provided to the facility and/or the technician.

As an example, the training is provided to the first facility after thefirst set of quality metrics are received and the subsequent set ofquality metrics are received after the training has been completed. Dueto the training being completed, the expectation is that the qualitymetrics for the facility will have improved. A comparison between thesubsequent set of quality metrics and the first set of quality metricscan either confirm or refute that expectation. For instance, if thetraining was for patient positioning, the patient positioning metricsfrom the first set of quality metrics are compared to the patientpositioning metrics from the subsequent set of quality metrics. If thesubsequent positioning metric improved, a positive effectiveness ratingis generated for the training to indicate that the training iseffective. If the subsequent positioning metric remained the same, aneutral effectiveness rating is generated for the training to indicatethat the training is ineffective. If the subsequent positioning metricworsened, a negative effectiveness rating is generated for the trainingto indicate that the training is counterproductive. The effectivenessrating may then be used as feedback to the central computing system ingenerating future trainings. For example, trainings that have received anegative effectiveness rating may no longer be generated and sent tofacilities, whereas trainings that have received a positiveeffectiveness rating may be more heavily weighted in generating futuretrainings.

In some examples, generating the effectiveness rating of the trainingmay account for the assessment scores of the technicians that receivedthe training. As discussed above, during the interactive trainings, anassessment score may be stored that indicates how well the technicianperformed during the training. If the assessment score is low for thetraining and the subsequent quality metric did not improve, the trainingitself may not be ineffective. Rather, the low-scoring technician mayneed additional more in-depth training and assistance. In such exampleswhere the assessment score is low, a lower weight may be assigned to theeffectiveness rating of the training.

At operation 850, a trend is determined for at least one quality metricbased on the comparison of the subsequent set of metrics and the priorset of quality metrics performed in operation 846. In some examples, thedetermination of the trend may be performed as part of operation 848.For instance, as discussed above, particular metrics may be compared toone another to determine whether the metric has improved or worsenedover time. That trend and the rate of the trend is determined inoperation 850. The rate of the trend may be based on the total change inthe particular metric over a designated period of time, such as days,weeks, months, quarters, or years.

At operation 852, a warning or notification may be generated based onthe trend determined in operation 850. The generation of the warning maybe based on the rate of the trend as well. For instance, if the trend isnegative and the rate of the trend is above a predetermined threshold, awarning may be generated and send to the facility that has the rapidlyworsening metric. Such a warning may prevent poor imaging proceduresbefore they occur because the facility can implement corrections uponreceiving the warning. The warning may also be based on a negative trendand a metric that is approaching a benchmark and/or federal guideline orregulation. For example, where a quality metric is within apredetermined threshold of a benchmark and/or federal guideline orregulation and the trend is negative, a warning may be generated andprovided to the facility. By providing such a warning, the facility isable to implement corrections before falling below the benchmark orbeing out of compliance with the issued guideline or regulation.

FIG. 9 illustrates an article of manufacture according to an embodiment.Storage medium 900 may comprise any computer-readable storage medium ormachine-readable storage medium, such as an optical, magnetic orsemiconductor storage medium. In some embodiments, storage medium 900may comprise a non-transitory storage medium. In various embodiments,storage medium 900 may comprise an article of manufacture. In someembodiments, storage medium 900 may store computer-executableinstructions, such as computer-executable instructions to implementlogic flow 900, for example. Examples of a computer-readable storagemedium or machine-readable storage medium may include any tangible mediacapable of storing electronic data, including volatile memory ornon-volatile memory, removable or non-removable memory, erasable ornon-erasable memory, writeable or re-writeable memory, and so forth.Examples of computer-executable instructions may include any suitabletype of code, such as source code, compiled code, interpreted code,executable code, static code, dynamic code, object-oriented code, visualcode, and the like. The embodiments are not limited to these examples.

FIG. 10 illustrates a block diagram of a centralized system 1000. Thecentralized system 1000 may implement some or all of the structureand/or operations for the web services system 1020 in a single computingentity, such as entirely within a single device 1010.

The device 1010 may comprise any electronic device capable of receiving,processing, and sending information for the web services system 1020.Examples of an electronic device may include without limitation animaging system, client device, a mobile computing device, a computer, aserver, a distributed computing system, multiprocessor systems, orcombination thereof. The embodiments are not limited in this context.

The device 1010 may execute processing operations or logic for the webservices system 1020 using a processing component 1030. The processingcomponent 1030 may comprise various hardware elements, softwareelements, or a combination of both. Examples of hardware elements mayinclude devices, logic devices, microprocessors, circuits, circuitelements (e.g., transistors, resistors, capacitors, inductors, and soforth), integrated circuits, and so forth. Examples of software elementsmay include software programs, machine programs, operating systemsoftware, middleware, firmware, functions, methods, procedures, softwareinterfaces, application program interfaces (API), words, values,symbols, or any combination thereof. Determining whether an embodimentis implemented using hardware elements and/or software elements may varyin accordance with any number of factors, such as desired computationalrate, power levels, heat tolerances, processing cycle budget, input datarates, output data rates, memory resources, data bus speeds and otherdesign or performance constraints, as desired for a givenimplementation.

The device 1010 may execute communications operations or logic for theweb services system 1020 using communications component 1040. Thecommunications component 1040 may implement any well-knowncommunications techniques and protocols, such as techniques suitable foruse with packet-switched networks (e.g., public networks such as theInternet, private networks such as an enterprise intranet, and soforth), circuit-switched networks (e.g., the public switched telephonenetwork), or a combination of packet-switched networks andcircuit-switched networks (with suitable gateways and translators). Thecommunications component 1040 may include various types of standardcommunication elements, such as one or more communications interfaces,network interfaces, wireless transmitters/receivers (transceivers),wired and/or wireless communication media, physical connectors, and soforth. By way of example, and not limitation, communication media 1009,1049 include wired communications media and wireless communicationsmedia,

The device 1010 may communicate with other devices 1005, 1045 over acommunications media 1009, 1049, respectively, using communicationssignals 1007, 1047, respectively, via the communications component 1040.The devices 1005, 1045, may be internal or external to the device 1010as desired for a given implementation.

For example, device 1005 may correspond to a client device such as aphone used by a user. Signals 1007 sent over media 1009 may thereforecomprise communication between the phone and the web services system1020 in winch the phone transmits a request and receives a web page orother data, in response.

FIG. 11 illustrates a block diagram of a distributed system 1 100. Thedistributed system 1100 may distribute portions of the structure and/oroperations for the disclosed embodiments across multiple computingentities. Examples of distributed system 00 may include withoutlimitation a client-server architecture, a peer-to-peer architecture, ashared database architecture, and other types of distributed systems.The embodiments are not limited in this context.

The distributed system 1100 may comprise a client device 1110 and aserver device 1140. In general, the client device 1110 and the serverdevice 1140 may be the same or similar to the client device 1010 asdescribed with reference to FIG. 10 . For instance, the client system1110 and the server system 1140 may each comprise a processing component1120, 1150 and a communications component 1130, 1160 which are the sameor similar to the processing component 1030 and the communicationscomponent 1040, respectively, as described with reference to FIG. 10 .In another example, the devices 1110, 1140 may communicate over acommunications media 1105 using communications signals 1107 via thecommunications components 1130, 1160.

The client device 1110 may comprise or employ one or more clientprograms that operate to perform various methodologies in accordancewith the described embodiments. In one embodiment, for example, theclient device 1110 may implement some steps described with respect toFIGS. 4-6 .

The server device 1140 may comprise or employ one or more serverprograms that operate to perform various methodologies in accordancewith the described embodiments. In one embodiment, for example, theserver device 40 may implement some steps described with respect toFIGS. 4-6 .

FIG. 12 illustrates an embodiment of an exemplary computing architecture1200 suitable for implementing various embodiments as previouslydescribed. In one embodiment, the computing architecture 1200 maycomprise or be implemented as part of an electronic device. Examples ofan electronic device may include those described herein. The embodimentsare not limited in this context.

As used in this application, the terms “system” and “component” areintended to refer to a computer-related entity, either hardware, acombination of hardware and software, software, or software inexecution, examples of which are provided by the exemplary computingarchitecture 1200. For example, a component can be, but is not limitedto being, a process running on a processor, a processor, a hard diskdrive, multiple storage drives (of optical and/or magnetic storagemedium), an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a server and the server can be a component. One or more componentscan reside within a process and/or thread of execution, and a componentcan be localized on one computer and/or distributed between two or morecomputers. Further, components may be communicatively coupled to eachother by various types of communications media to coordinate operations.The coordination may involve the unidirectional or bi-directionalexchange of information. For instance, the components may communicateinformation in the form of signals communicated over the communicationsmedia. The information can be implemented as signals allocated tovarious signal lines. In such allocations, each message is a signal.Further embodiments, however, may alternatively employ data messages.Such data messages may be sent across various connections. Exemplaryconnections include parallel interfaces, serial interfaces, and businterfaces.

The computing architecture 1200 includes various common computingelements, such as one or more processors, multi-core processors,co-processors, memory units, chipsets, controllers, peripherals,interfaces, oscillators, timing devices, video cards, audio cards,multimedia input/output (I/O) components, power supplies, and so forth.The embodiments, however, are not limited to implementation by thecomputing architecture 1500.

As shown in FIG. 12 , the computing architecture 1200 comprises aprocessing unit 1204, a system memory 1206 and a system bus 1208. Dualmicroprocessors, multi-core processors, and other multi-processorarchitectures may also be employed as the processing unit 1204.

The system bus 1208 provides an interface for system componentsincluding, but not limited to, the system memory 1206 to the processingunit 1204. The system bus 1208 can be any of several types of busstructure that may further interconnect to a memory bus (with or withouta memory controller), a peripheral bus, and a local bus using any of avariety of commercially available bus architectures. Interface adaptersmay connect to the system bus 1208 via a slot architecture, for example.

The computing architecture 1200 may comprise or implement variousarticles of manufacture. An article of manufacture may comprise acomputer-readable storage medium to store logic, as described above withrespect to FIG. 9 .

The system memory 1206 may include various types of computer-readablestorage media in the form of one or more higher speed memory units, suchas read-only memory (ROM), random-access memory (RAM), dynamic RAM(DRAM), solid state memory devices (e.g., USB memory, solid state drives(SSD) and any other type of storage media suitable for storinginformation). In the illustrated embodiment shown in FIG. 12 , thesystem memory 1206 can include non-volatile memory 1210 and/or volatilememory 1213. A basic input/output system (BIOS) can be stored in thenon-volatile memory 1210.

The computer 1202 may include various types of computer-readable storagemedia in the form of one or more lower speed memory units, including aninternal (or external) hard disk drive (HDD) 1214, a magnetic floppydisk drive (FDD) 1216 to read from or write to a removable magnetic disk1218, and an optical disk drive 1220 to read from or write to aremovable optical disk 1222 (e.g., a CD-ROM, DVD, or Blu-ray). The HDD1214, FDD 1216 and optical disk drive 1220 can be connected to thesystem bus 1208 by a HDD interface 1224, an FDD interface 1226 and anoptical drive interface 1228, respectively. The HDD interface 1224 forexternal drive implementations can include at least one or both ofUniversal Serial Bus (USB) and IEEE 1394 interface technologies.

The drives and associated computer-readable media provide volatileand/or nonvolatile storage of data, data, structures,computer-executable instructions, and so forth. For example, a number ofprogram modules can be stored in the drives and memory units 1210, 1213,including an operating system 1230, one or more application programs1232, other program modules 1234, and program data 1236. In oneembodiment, the one or more application programs 1232, other programmodules 1234, and program data 1236 can include, for example, thevarious applications and/or components to implement the disclosedembodiments.

A user can enter commands and information into the computer 1202 throughone or more wire/wireless input devices, for example, a keyboard 1238and a pointing device, such as a mouse 1240. Other input devices mayinclude microphones, infra-red (IR) remote controls, radio-frequency(RF) remote controls, game pads, stylus pens, card readers, dongles,finger print readers, gloves, graphics tablets, joysticks, keyboards,retina readers, touch screens (e.g., capacitive, resistive, etc.),trackballs, trackpads, sensors, styluses, and the like. These and otherinput devices are often connected to the processing unit 1204 through aninput device interface 1242 that is coupled to the system bus 1208, butcan be connected by other interfaces such as a parallel port, IEEE 1394serial port, a game port, a USB port, an IR interface, and so forth.

A display 1244 is also connected to the system bus 1208 via aninterface, such as a video adaptor 1246. The display 1244 may beinternal or external to the computer 1202. In addition to the display1244, a computer typically includes other peripheral output devices,such as speakers, printers, and so forth.

The computer 1202 may operate in a networked environment using logicalconnections via wire and/or wireless communications to one or moreremote computers, such as a remote computer 248. The remote computer1248 can be a workstation, a server computer, a router, a personalcomputer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer1202, although, for purposes of brevity, only a memory/storage device1250 is illustrated. The logical connections depicted includewire/wireless connectivity to a local area network (LAN) 1252 and/orlarger networks, for example, a wide area network (WAN) 1254. Such LANand WAN networking environments are commonplace in offices andcompanies, and facilitate enterprise-wide computer networks, such asintranets, all of which may connect to a global communications network,for example, the Internet.

When used in a LAN networking environment, the computer 1202 isconnected to the LAN 1252 through a wire and/or wireless communicationnetwork interface or adaptor 1256. The adaptor 1256 can facilitate wireand/or wireless communications to the LAN 1252, which may also include awireless access point disposed thereon for communicating with thewireless functionality of the adaptor 1256.

When used in a WAN networking environment, the computer 1202 can includea modem 1258, or is connected to a communications server on the WAN1254, or has other means for establishing communications over the WAN1254, such as by way of the Internet. The modem 1258, which can beinternal or external and a wire and/or wireless device, connects to thesystem bus 1208 via the input device interface 1242. In a networkedenvironment, program modules depicted relative to the computer 1202, orportions thereof, can be stored in the remote memory/storage device1250. It will be appreciated that the network connections shown areexemplary and other means of establishing a communications link betweenthe computers can be used.

The computer 1202 is operable to communicate with wire and wirelessdevices or entities using the IEEE 802 family of standards, such aswireless devices operatively disposed in wireless communication (e.g.,IEEE 802.1 1 over-the-air modulation techniques). This includes at leastWi-Fi (or Wireless Fidelity), WiMax, and Bluetooth™ wirelesstechnologies, among others.

FIG. 13 illustrates a block diagram of an exemplary communicationsarchitecture 300 suitable for implementing various embodiments aspreviously described. The communications architecture 1300 includesvarious common communications elements, such as a transmitter, receiver,transceiver, radio, network interface, baseband processor, antenna,amplifiers, filters, power supplies, and so forth. The embodiments,however, are not limited to implementation by the communicationsarchitecture 1300.

As shown in FIG. 13 , the communications architecture 1300 comprisesincludes one or more clients 1310 and servers 1340. The clients 1310 mayimplement the client device 1110, for example. The servers 1340 mayimplement the server device 1140, for example. The clients 1310 and theservers 340 are operatively connected to one or more respective clientdata stores 1320 and server data stores 1350 that can be employed tostore information local to the respective clients 1310 and servers 1340,such as cookies and/or associated contextual information.

The clients 1310 and the servers 1340 may communicate informationbetween each other using a communication framework 1330. Thecommunications framework 1330 may implement any well-knowncommunications techniques and protocols. The communications framework1330 may be implemented as a packet-switched network (e.g., publicnetworks such as the Internet, private networks such as an enterpriseintranet, and so forth), a circuit-switched network (e.g., the publicswitched telephone network), or a combination of a packet-switchednetwork and a circuit-switched network (with suitable gateways andtranslators).

The communications framework 1330 may implement various networkinterfaces arranged to accept communicate, and connect to acommunications network. A network interface may be regarded as aspecialized form of an input output interface. Network interfaces mayemploy connection protocols including without limitation direct connect,Ethernet, wireless network interfaces, cellular network interfaces, andthe like.

Some embodiments may be described using the expression “one embodiment”or “an embodiment” along with their derivatives. These terms mean that aparticular feature, structure, or characteristic described in connectionwith the embodiment is included in at least one embodiment. Theappearances of the phrase “in one embodiment” in various places in thespecification are not necessarily all referring to the same embodiment.Further, some embodiments may be described using the expression“coupled” and “connected” along with their derivatives. These terms arenot necessarily intended as synonyms for each other. For example, someembodiments may be described using the terms “connected” and/or“coupled” to indicate that two or more elements are in direct physicalor electrical contact with each other. The term “coupled,” however, mayalso mean that two or more elements are not in direct contact with eachother, but yet still co-operate or interact with each other.

A procedure is here, and generally, conceived to be a self-consistentsequence of operations leading to a desired result. These operations arethose requiring physical manipulations of physical quantities. Usually,though not necessarily, these quantities take the form of electrical,magnetic or optical signals capable of being stored, transferred,combined, compared, and otherwise manipulated. It proves convenient attimes, principally for reasons of common usage, to refer to thesesignals as bits, values, elements, symbols, characters, terms, numbers,or the like. It should be noted, however, that all of these and similarterms are to be associated with the appropriate physical quantities andare merely convenient labels applied to those quantities.

Further, the manipulations performed are often referred to in terms,such as adding or comparing, which are commonly associated with mentaloperations performed by a human operator. No such capability of a humanoperator is necessary, or desirable in most cases, in any of theoperations described herein which form part of one or more embodiments.Rather, the operations are machine operations. Useful machines forperforming operations of various embodiments include general purposedigital computers or similar devices.

Various embodiments also relate to apparatus or systems for performingthese operations. This apparatus may be specially constructed for therequired purpose or it may comprise a general purpose computer asselectively activated or reconfigured by a computer program stored inthe computer. The procedures presented herein are not inherently relatedto a particular computer or other apparatus. Various general purposemachines may be used with programs written in accordance with theteachings herein, or it may prove convenient to construct morespecialized apparatus to perform the required method steps. The requiredstructure for a variety of these machines will appear from thedescription given.

In the foregoing Detailed Description, it can be seen that variousfeatures are grouped together in a single embodiment for the purpose ofstreamlining the disclosure. This method of disclosure is not to beinterpreted as reflecting an intention that the claimed embodimentsrequire more features than are expressly recited in each claim. Rather,as the following claims reflect, inventive subject matter lies in lessthan all features of a single disclosed embodiment. Thus the followingclaims are hereby incorporated into the Detailed Description, with eachclaim standing on its own as a separate embodiment. In the appendedclaims, the terms “including” and “in which” are used as theplain-English equivalents of the respective terms “comprising” and“wherein,” respectively. Moreover, the terms “first,” “second,” “third,”and so forth, are used merely as labels, and are not intended to imposenumerical requirements on their objects.

What has been described above includes examples of the disclosedarchitecture. It is, of course, not possible to describe everyconceivable combination of components and/or methodologies, but one ofordinary skill in the art may recognize that many further combinationsand permutations are possible.

1-20. (canceled)
 21. A method for determining a corrective action toimprove medical imaging quality, the method comprising: generating, by aforce sensor, a force signal indicating a measure of force applied tobreast tissue being compressed against a breast support platform tocapture an image of the breast tissue; filtering, by a movementdetection circuit, a movement signal from the force signal indicating ameasure of movement of the compressed breast tissue; receiving, by animage correction module, the movement signal; determining, by the imagecorrection module, a type of movement based upon the received movementsignal, wherein the type of movement is selected from a group includingregular movement and irregular movement, wherein regular movement isidentified by being repetitive within a regular time interval; andperforming, by the image correction module, the corrective action basedupon the type of movement determined.
 22. The method of claim 21,further comprising making a movement determination that the movementsignal exceeds a movement threshold, wherein determining the type ofmovement is further based upon the movement determination.
 23. Themethod of claim 22, wherein the movement determination comprises one ormore of an indication that a movement has been detected, a movementvalue, a timestamp, and a frame identifier.
 24. The method of claim 21,wherein determining the type of movement is further based on a number ofimage frames in which the movement signal is detected.
 25. The method ofclaim 21, further comprising determining the movement signal is aregular movement and, in response to determining the movement signal isthe regular movement, the corrective action comprises configuring theimaging detector to synchronize image capture with the regular movement.26. The method of claim 25, wherein configuring the imaging detector tosynchronize the image capture with the regular movement comprisespausing image capture during the regular time interval when the movementsignal is detected.
 27. The method of claim 21, wherein an irregularmovement is further identified as a single irregular movement based onthe movement signal comprising a single instance.
 28. The method ofclaim 27, wherein identifying the irregular movement as the singleirregular movement is further based on the movement signal beinglocalized in a single image frame and the corrective action comprisesremoving the single image frame from a final imaging sequence.
 29. Themethod of claim 21, wherein an irregular movement is further identifiedas being a continuous irregular movement.
 30. The method of claim 21,further comprising determining the movement signal is an irregularmovement and, in response to determining the movement signal is theirregular movement, the corrective action comprises delaying imagecapture by the imaging detector for a predetermined period of time. 31.The method of claim 30, further comprising: determining a duration ofthe movement signal exceeds the predetermined period of time fordelaying the image capture; and in response to determining the durationof the movement signal exceeds the predetermined period of time,extending delay of image capture.
 32. The method of claim 31, furthercomprising: determining the duration of the movement signal exceeds atime period threshold; and in response to determining the duration ofthe movement signal exceeds the time period threshold, cancelling theimage capture.
 33. The method of claim 21, wherein force sensor isintegrated with a medical imaging system and the method furthercomprises: submitting the movement signal, the type of movement, and thecorrective action as quality metrics; and aggregating the qualitymetrics into a set of quality metrics associated with a facility housingthe medical imaging system.
 34. A system for determining a correctiveaction to improve medical imaging quality, the method comprising: animaging detector for capturing an image of breast tissue; a force sensorfor generating a force signal indicating a measure of force applied tothe breast tissue compressed against the imaging detector; at least oneprocessor; memory, operatively coupled to the at least one processor,storing instructions that when executed by the at least one processor,cause the system to perform a set of operations, comprising: receivingthe force signal from the force sensor; filtering a movement signal fromthe force signal indicating a measure of movement of the compressedbreast tissue; determining a type of movement based upon the receivedmovement signal, wherein the type of movement is selected from a groupincluding regular movement and irregular movement, wherein regularmovement is identified by being repetitive within a regular timeinterval; and performing the corrective action based upon the type ofmovement determined.
 35. The system of claim 34, further comprisingmaking a movement determination that the movement signal exceeds amovement threshold, wherein determining the type of movement is furtherbased upon the movement determination, wherein the movementdetermination comprises one or more of an indication that movement hasbeen detected, a movement value, a timestamp, a frame identifier, and anumber of image frames in which the movement signal is detected.
 36. Thesystem of claim 34, further comprising determining the movement signalis a regular movement and, in response to determining the movementsignal is the regular movement, the corrective action comprisesconfiguring the imaging detector to synchronize image capture with theregular movement.
 37. The system of claim 36, wherein configuring theimaging detector to synchronize the image capture with the regularmovement comprises skipping image capture during the regular timeinterval when the movement signal is detected.
 38. The system of claim34, wherein an irregular movement is further identified as a singleirregular movement based on the movement signal not being repeated orthe movement signal being localized in a single image frame; and whereinthe corrective action comprises removing the single image frame from afinal imaging sequence.
 39. The system of claim 34, wherein an irregularmovement is further identified as being a continuous irregular movementand, in response to determining the movement signal is the continuesirregular movement, the corrective action comprises delaying imagecapture by the imaging detector for a predetermined period of time andthe set of operations further comprises: determining a duration of themovement signal exceeds the predetermined period of time for delayingthe image capture; in response to determining the duration of themovement signal exceeds the predetermined period of time, extendingdelay of image capture; determining the duration of the movement signalexceeds a time period threshold; and in response to determining theduration of the movement signal exceeds the time period threshold,cancelling the image capture.
 40. The system of claim 34, wherein theset of operations further comprises: submitting the movement signal, thetype of movement, and the corrective action as quality metrics; andaggregating the quality metrics into a set of quality metrics associatedwith a facility housing the imaging detector.